AutoStyle-TTS: Retrieval-Augmented Generation based Automatic Style Matching Text-to-Speech Synthesis

• URL: http://arxiv.org/abs/2504.10309v1
• Date: Mon, 14 Apr 2025 15:18:59 GMT
• Title: AutoStyle-TTS: Retrieval-Augmented Generation based Automatic Style Matching Text-to-Speech Synthesis
• Authors: Dan Luo, Chengyuan Ma, Weiqin Li, Jun Wang, Wei Chen, Zhiyong Wu,
• Abstract summary: This study proposes a text-to-speech (TTS) framework based on Retrieval-Augmented Generation (RAG) technology.<n>We have constructed a speech style knowledge database containing high-quality speech samples in various contexts.<n>This scheme uses embeddings, extracted by Llama, PER-LLM-Embedder,and Moka, to match with samples in the knowledge database, selecting the most appropriate speech style for synthesis.
• Score: 19.141058309358424
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: With the advancement of speech synthesis technology, users have higher expectations for the naturalness and expressiveness of synthesized speech. But previous research ignores the importance of prompt selection. This study proposes a text-to-speech (TTS) framework based on Retrieval-Augmented Generation (RAG) technology, which can dynamically adjust the speech style according to the text content to achieve more natural and vivid communication effects. We have constructed a speech style knowledge database containing high-quality speech samples in various contexts and developed a style matching scheme. This scheme uses embeddings, extracted by Llama, PER-LLM-Embedder,and Moka, to match with samples in the knowledge database, selecting the most appropriate speech style for synthesis. Furthermore, our empirical research validates the effectiveness of the proposed method. Our demo can be viewed at: https://thuhcsi.github.io/icme2025-AutoStyle-TTS

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